Inspection, visualisation and analysis of quantitative proteomics data

Laurent Gatto                      Computational Proteomics Unit
https://lgatto.github.io           University of Cambridge

Slides: http://bit.ly/qprotda – all material

Acknowledgements BBSRC for funding; Sebastian Gibb and Lisa Breckels for coding.

(Last update Thu Mar 31 17:18:59 2016)

These slides are available under a creative common CC-BY license. You are free to share (copy and redistribute the material in any medium or format) and adapt (remix, transform, and build upon the material) for any purpose, even commercially.

Content

1. Introduction on data analysis
2. Quantitative proteomics data analysis - overview
3. Visualisation
4. Quantitative proteomics data analysis - examples
5. Data analysis
6. References and resources

What is data analysis

Data analysis is the process by which data becomes understanding, knowledge and insight. Hadley Wickham

The ability to prepare and explore data, identify patterns (good and pathological ones) and convincingly demonstrate that the pattern are genuine (rather than random).

Requires flexible tools.

Why programme

• Reproducibility/replicability
• Automation
• Plain text: tracking, re-use, share, communicable

But:

• Learning curve (although, I would argue that the reflection about the data and analysis is the really challenging part)

To analyse data, you need

• Programming skills
• Be a statistician/machine learner

To analyse data, you need

• Programming skills
• Be a statistician/machine learner
• Knowledge about the domain
• Know about your data
• Be able to manipulate/visualise it
• How good do we need to be?

To analyse data, you need

• Programming skills
• Be a statistician/machine learner
• Knowledge about the domain
• Know about your data
• Be able to manipulate/visualise it
• How good do we need to be? Good enough

Quantitative proteomics data analysis

• What data do we have?
• What analyses can we apply?
• Track/check the data throughout the analysis.

Quantitative proteomics data analysis

• Data Ratios or intenstities - do not let the software decide for you!
• Replicates: technical and biological
• Variability: technical vs biological
• Normalisation: remove unwanted (technical) variation while retaining biological variability.

Visualisation

A picture is worth a thousand words.

Visualization can surprise you, but it doesn’t scale well. Modeling scales well, but it can’t surprise you. Hadley Wickham

Graphics reveal data.

Proteomics data analysis - examples

• chomatogram
• xic
• spectum (iTRAQ)
• peptide-protein graph
• see NOTES
• see Statistics primer practical
• MA plots

Data analysis tools

should enables you to manipulate your data, give some guarantees about the integrity of the data, support effective extract/subset components of the data, visualise them, enable transformation of the data, give access to infrastucture for statistical analysis, and enable annotation of the data.

Data analysis tools to

• Store data as computational objects (rigorously defined data structures): knowing where to find things
• Consistency in data structures (and conventions)
• Easily manipluate/transform/visualise these data (Application programming interface)

The MSnSet class for quantitative data

Can be subsetted, transformed, visualised, annotated, statistics, …

References, resources

• Visualisation of proteomics data using R and Bioconductor
• Using R and Bioconductor for proteomics data analysis
• RforProteomics: http://bioconductor.org/packages/RforProteomics
• R/Bioconductor workflow
• Teaching material for R and more
• Workshops: Software and Data Carpentry, Data Programmers

Thank you for your attention